We observed that, by evaluating utilities at the frontiers, the selected candidate goal will lead to more immediate information in general. However, since the information is estimated blindly, it will lead to wrong decisions by selecting a goal that actually provides utilities. By utilizing information prediction, information gains at frontiers are estimated much more accurately, which directly improves the quality of selected goals, leading to faster exploration and shorter path lengths. Instead of maximizing immediate information gain, FUEL minimizes the length of the global tour that covers all frontiers. However, we observed that this coverage tour optimization sometimes leads to repeated visits of the explored regions as shown in Fig. REF . This results in a long path length in the structured indoor setting, which can be seen in Tab. REF . However, our proposed method uses semantic information to avoid repeated visits to aoi and to plan informative trajectories during exploration. As a result, our method achieves a \(\textbf {24\%}\)  shorter overall path length with a comparable exploration time compared with the state-of-the-art method [1]}. We also observed that our proposed method outperforms other methods in Tab. REF  more as the environment size increase.
